Product Page Classification

ABSTRACT

In one embodiment, a method includes extracting a document object model (DOM) for a content page. The DOM comprises a hierarchical tree-based data structure. The method also includes traversing the DOM to identify, in the content page, candidate features of a product page. The method further includes for each of the candidate features, determine candidate feature attributes based on a respective context of the candidate feature in the DOM. The method additionally includes determining whether the content page qualifies as a product page based on whether the candidate features fulfill a required set of characteristics for a product page.

TECHNICAL FIELD

This disclosure generally relates to classifying pages, and specifically classifying content pages as product pages.

BACKGROUND

A social-networking system, which may include a social-networking website, may enable its users (such as persons or organizations) to interact with it and with each other through it. The social-networking system may, with input from a user, create and store in the social-networking system a user profile associated with the user. The user profile may include demographic information, communication-channel information, and information on personal interests of the user. The social-networking system may also, with input from a user, create and store a record of relationships of the user with other users of the social-networking system, as well as provide services (e.g., wall posts, photo-sharing, event organization, messaging, games, or advertisements) to facilitate social interaction between or among users.

The social-networking system may send over one or more networks content or messages related to its services to a mobile or other computing device of a user. A user may also install software applications on a mobile or other computing device of the user for accessing a user profile of the user and other data within the social-networking system. The social-networking system may generate a personalized set of content objects to display to a user, such as a newsfeed of aggregated stories of other users connected to the user.

Social-graph analysis views social relationships in terms of network theory consisting of nodes and edges. Nodes represent the individual actors within the networks, and edges represent the relationships between the actors. The resulting graph-based structures are often very complex. There can be many types of nodes and many types of edges for connecting nodes. In its simplest form, a social graph is a map of all of the relevant edges between all the nodes being studied.

A mobile computing device—such as a smartphone, tablet computer, or laptop computer—may include functionality for determining its location, direction, or orientation, such as a GPS receiver, compass, gyroscope, or accelerometer. Such a device may also include functionality for wireless communication, such as BLUETOOTH communication, near-field communication (NFC), or infrared (IR) communication or communication with a wireless local area networks (WLANs) or cellular-telephone network. Such a device may also include one or more cameras, scanners, touchscreens, microphones, or speakers. Mobile computing devices may also execute software applications, such as games, web browsers, or social-networking applications. With social-networking applications, users may connect, communicate, and share information with other users in their social networks.

SUMMARY OF PARTICULAR EMBODIMENTS

In certain embodiments, a document object model (DOM) is extracted for a content page, such as a web page or an extensible markup language (XML) product catalog associated with an electronic commerce (e-commerce) entity. The DOM is a hierarchical tree-based data structure that represents each element of the content page as a “node.” In certain embodiments, “features” refer to DOM nodes or page elements and their associated characteristics and functionality that represent a portion of a content page. Candidate features in the DOM are identified, for example, based on adherence to a set of characteristics for a product feature. In certain embodiments, candidate feature attributes are determined for each of the candidate features based on a context of the candidate feature in the DOM. For example, the context of the features can include information regarding relationships between features, positioning of features with respect to the content page, feature size, type, and the like. In certain embodiments, a determination is made as to whether the content page is a product page. Product pages can be associated with products, such as listing products or displaying product details. In certain embodiments, the determination is made based on whether the candidate features fulfill a set of required characteristics for a product page. For example, the determination is made based on whether the candidate features fit a template of features that are generally associated with product pages.

In certain embodiments, e-commerce entities are identified and associated webpages are crawled to identify product pages. Item pages include any content page that is associated with at least one highlighted item, product, reservation, show, or the like that is being offered. A DOM is extracted from each webpage, and candidate features (e.g., groupings of DOM nodes or content elements that are likely associated with a product or product listing) are identified. For example, a user interface control is identified in the DOM. The control includes layers of user interface elements, such as an image, link, positioning and display containers, and the like. Each interface element is represented as a node in the DOM. Together the nodes are associated as a user interface control. In certain embodiments, groupings of interface elements, such as controls, are considered features. A feature may include one or more controls. For example, product listing pages often have a navigation section that controls the number of products listed per page, and the page number within the total set of product listings. The navigation section is comprised of two buttons or links (i.e., “Back” and “Next” buttons), and a combo box control for selecting the number of products per page to display. Details regarding this and other identified controls or features in the DOM are collected.

In certain embodiments, a determination is made as to whether the content page qualifies as a product page based on whether the features fulfill a required set of characteristics for a product page. The set of characteristics often includes one or more rules regarding feature context. In certain embodiments, context can be described by a template. The template contains features that are often found in product pages, along with generalized relationships and positioning information for the features. In certain embodiments, rules specify relationships between features that are typically found in a product page. For example, a rule specifies that product pages often contain a navigation feature and multiple product listings. The rule specifies that the navigation feature controls display of the listed products. As another example, other product features such as title, image, and description elements are specified in a template. In certain embodiments, feature links can also indicate an association with a product page. For example, certain product details pages include a circular product title link (e.g., a product title link that re-directs to the same page). Positioning ratios can also be compared to determine whether a set of features indicates that the content page is a product page.

In certain embodiments, product pages are classified based on particular features, attributes, and context extracted from them. For example, pages can be classified as a (1) product page, (2) non-product page, (3) multiple product listing page, which includes (a) category product listing pages and (b) search result product listing pages, and (4) other pages, such as home or about pages. The classifier crawls web pages to identify page attributes. The page attributes are aggregated and compared to determine a type of page that is being analyzed. (e.g., product page or product listing page). Particular page types can be used in the product crawl to iterate through product listings, extract links to product pages, or identify related products. Rules define characteristics for identifying each type of page. Characteristics are applied to extracted features to make a determination as to page type. In certain embodiments, information extraction processes are optimized based on the determined page types.

The embodiments disclosed above are only examples, and the scope of this disclosure is not limited to them. Particular embodiments may include all, some, or none of the components, elements, features, functions, operations, or steps of the embodiments disclosed above. Embodiments according to the invention are in particular disclosed in the attached claims directed to a method, a storage medium, a system and a computer program product, wherein any feature mentioned in one claim category, e.g. method, can be claimed in another claim category, e.g. system, as well. The dependencies or references back in the attached claims are chosen for formal reasons only. However any subject matter resulting from a deliberate reference back to any previous claims (in particular multiple dependencies) can be claimed as well, so that any combination of claims and the features thereof are disclosed and can be claimed regardless of the dependencies chosen in the attached claims. The subject-matter which can be claimed comprises not only the combinations of features as set out in the attached claims but also any other combination of features in the claims, wherein each feature mentioned in the claims can be combined with any other feature or combination of other features in the claims. Furthermore, any of the embodiments and features described or depicted herein can be claimed in a separate claim and/or in any combination with any embodiment or feature described or depicted herein or with any of the features of the attached claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example network environment associated with a social-networking system.

FIG. 2 illustrates an example social graph.

FIG. 3 illustrates an example flow chart of a method for classifying product pages.

FIG. 4 illustrates an example product details page.

FIG. 5 illustrates an example product listing page featuring product listings.

FIG. 6 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

FIG. 1 illustrates an example network environment 100 associated with a social-networking system. Network environment 100 includes a client system 130, a social-networking system 160, and a third-party system 170 connected to each other by a network 110. Although FIG. 1 illustrates a particular arrangement of a client system 130, a social-networking system 160, a third-party system 170, and a network 110, this disclosure contemplates any suitable arrangement of a client system 130, a social-networking system 160, a third-party system 170, and a network 110. As an example and not by way of limitation, two or more of a client system 130, a social-networking system 160, and a third-party system 170 may be connected to each other directly, bypassing a network 110. As another example, two or more of a client system 130, a social-networking system 160, and a third-party system 170 may be physically or logically co-located with each other in whole or in part. Moreover, although FIG. 1 illustrates a particular number of client systems 130, social-networking systems 160, third-party systems 170, and networks 110, this disclosure contemplates any suitable number of client systems 130, social-networking systems 160, third-party systems 170, and networks 110. As an example and not by way of limitation, network environment 100 may include multiple client systems 130, social-networking systems 160, third-party systems 170, and networks 110.

This disclosure contemplates any suitable network 110. As an example and not by way of limitation, one or more portions of a network 110 may include an ad hoc network, an intranet, an extranet, a virtual private network (VPN), a local area network (LAN), a wireless LAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), a portion of the Internet, a portion of the Public Switched Telephone Network (PSTN), a cellular telephone network, or a combination of two or more of these. A network 110 may include one or more networks 110.

Links 150 may connect a client system 130, a social-networking system 160, and a third-party system 170 to a communication network 110 or to each other. This disclosure contemplates any suitable links 150. In particular embodiments, one or more links 150 include one or more wireline (such as for example Digital Subscriber Line (DSL) or Data Over Cable Service Interface Specification (DOCSIS)), wireless (such as for example Wi-Fi or Worldwide Interoperability for Microwave Access (WiMAX)), or optical (such as for example Synchronous Optical Network (SONET) or Synchronous Digital Hierarchy (SDH)) links. In particular embodiments, one or more links 150 each include an ad hoc network, an intranet, an extranet, a VPN, a LAN, a WLAN, a WAN, a WWAN, a MAN, a portion of the Internet, a portion of the PSTN, a cellular technology-based network, a satellite communications technology-based network, another link 150, or a combination of two or more such links 150. Links 150 need not necessarily be the same throughout a network environment 100. One or more first links 150 may differ in one or more respects from one or more second links 150.

In particular embodiments, a client system 130 may be an electronic device including hardware, software, or embedded logic components or a combination of two or more such components and capable of carrying out the appropriate functionalities implemented or supported by a client system 130. As an example and not by way of limitation, a client system 130 may include a computer system such as a desktop computer, notebook or laptop computer, netbook, a tablet computer, e-book reader, GPS device, camera, personal digital assistant (PDA), handheld electronic device, cellular telephone, smartphone, other suitable electronic device, or any suitable combination thereof. This disclosure contemplates any suitable client systems 130. A client system 130 may enable a network user at a client system 130 to access a network 110. A client system 130 may enable its user to communicate with other users at other client systems 130.

In particular embodiments, a client system 130 may include a web browser 132, such as MICROSOFT INTERNET EXPLORER, GOOGLE CHROME or MOZILLA FIREFOX, and may have one or more add-ons, plug-ins, or other extensions, such as TOOLBAR or YAHOO TOOLBAR. A user at a client system 130 may enter a Uniform Resource Locator (URL) or other address directing a web browser 132 to a particular server (such as server 162, or a server associated with a third-party system 170), and the web browser 132 may generate a Hyper Text Transfer Protocol (HTTP) request and communicate the HTTP request to server. The server may accept the HTTP request and communicate to a client system 130 one or more Hyper Text Markup Language (HTML) files responsive to the HTTP request. The client system 130 may render a webpage based on the HTML files from the server for presentation to the user. This disclosure contemplates any suitable webpage files. As an example and not by way of limitation, webpages may render from HTML files, Extensible Hyper Text Markup Language (XHTML) files, or Extensible Markup Language (XML) files, according to particular needs. Such pages may also execute scripts such as, for example and without limitation, those written in JAVASCRIPT, JAVA, MICROSOFT SILVERLIGHT, combinations of markup language and scripts such as AJAX (Asynchronous JAVASCRIPT and XML), and the like. Herein, reference to a webpage encompasses one or more corresponding webpage files (which a browser may use to render the webpage) and vice versa, where appropriate. More information on webpages may be found in U.S. patent application Ser. No. 11/856,202, filed 17 Sep. 2007, which is incorporated by reference.

In particular embodiments, the social-networking system 160 may be a network-addressable computing system that can host an online social network. The social-networking system 160 may generate, store, receive, and send social-networking data, such as, for example, user-profile data, concept-profile data, social-graph information, or other suitable data related to the online social network. The social-networking system 160 may be accessed by the other components of network environment 100 either directly or via a network 110. As an example and not by way of limitation, a client system 130 may access the social-networking system 160 using a web browser 132, or a native application associated with the social-networking system 160 (e.g., a mobile social-networking application, a messaging application, another suitable application, or any combination thereof) either directly or via a network 110. In particular embodiments, the social-networking system 160 may include one or more servers 162. Each server 162 may be a unitary server or a distributed server spanning multiple computers or multiple datacenters. Servers 162 may be of various types, such as, for example and without limitation, web server, news server, mail server, message server, advertising server, file server, application server, exchange server, database server, proxy server, another server suitable for performing functions or processes described herein, or any combination thereof. In particular embodiments, each server 162 may include hardware, software, or embedded logic components or a combination of two or more such components for carrying out the appropriate functionalities implemented or supported by server 162. In particular embodiments, the social-networking system 160 may include one or more data stores 164. Data stores 164 may be used to store various types of information. In particular embodiments, the information stored in data stores 164 may be organized according to specific data structures. In particular embodiments, each data store 164 may be a relational, columnar, correlation, or other suitable database. Although this disclosure describes or illustrates particular types of databases, this disclosure contemplates any suitable types of databases. Particular embodiments may provide interfaces that enable a client system 130, a social-networking system 160, or a third-party system 170 to manage, retrieve, modify, add, or delete, the information stored in data store 164.

In particular embodiments, the social-networking system 160 may store one or more social graphs in one or more data stores 164. In particular embodiments, a social graph may include multiple nodes—which may include multiple user nodes (each corresponding to a particular user) or multiple concept nodes (each corresponding to a particular concept)—and multiple edges connecting the nodes. The social-networking system 160 may provide users of the online social network the ability to communicate and interact with other users. In particular embodiments, users may join the online social network via the social-networking system 160 and then add connections (e.g., relationships) to a number of other users of the social-networking system 160 whom they want to be connected to. Herein, the term “friend” may refer to any other user of the social-networking system 160 with whom a user has formed a connection, association, or relationship via the social-networking system 160.

In particular embodiments, the social-networking system 160 may provide users with the ability to take actions on various types of items or objects, supported by the social-networking system 160. As an example and not by way of limitation, the items and objects may include groups or social networks to which users of the social-networking system 160 may belong, events or calendar entries in which a user might be interested, computer-based applications that a user may use, transactions that allow users to buy or sell items via the service, interactions with advertisements that a user may perform, or other suitable items or objects. A user may interact with anything that is capable of being represented in the social-networking system 160 or by an external system of a third-party system 170, which is separate from the social-networking system 160 and coupled to the social-networking system 160 via a network 110.

In particular embodiments, the social-networking system 160 may be capable of linking a variety of entities. As an example and not by way of limitation, the social-networking system 160 may enable users to interact with each other as well as receive content from third-party systems 170 or other entities, or to allow users to interact with these entities through an application programming interfaces (API) or other communication channels.

In particular embodiments, a third-party system 170 may include one or more types of servers, one or more data stores, one or more interfaces, including but not limited to APIs, one or more web services, one or more content sources, one or more networks, or any other suitable components, e.g., that servers may communicate with. A third-party system 170 may be operated by a different entity from an entity operating the social-networking system 160. In particular embodiments, however, the social-networking system 160 and third-party systems 170 may operate in conjunction with each other to provide social-networking services to users of the social-networking system 160 or third-party systems 170. In this sense, the social-networking system 160 may provide a platform, or backbone, which other systems, such as third-party systems 170, may use to provide social-networking services and functionality to users across the Internet.

In particular embodiments, a third-party system 170 may include a third-party content object provider. A third-party content object provider may include one or more sources of content objects, which may be communicated to a client system 130. As an example and not by way of limitation, content objects may include information regarding things or activities of interest to the user, such as, for example, movie show times, movie reviews, restaurant reviews, restaurant menus, product information and reviews, or other suitable information. As another example and not by way of limitation, content objects may include incentive content objects, such as coupons, discount tickets, gift certificates, or other suitable incentive objects.

In particular embodiments, the social-networking system 160 also includes user-generated content objects, which may enhance a user's interactions with the social-networking system 160. User-generated content may include anything a user can add, upload, send, or “post” to the social-networking system 160. As an example and not by way of limitation, a user communicates posts to the social-networking system 160 from a client system 130. Posts may include data such as status updates or other textual data, location information, photos, videos, links, music or other similar data or media. Content may also be added to the social-networking system 160 by a third-party through a “communication channel,” such as a newsfeed or stream.

In particular embodiments, the social-networking system 160 may include a variety of servers, sub-systems, programs, modules, logs, and data stores. In particular embodiments, the social-networking system 160 may include one or more of the following: a web server, action logger, API-request server, relevance-and-ranking engine, content-object classifier, notification controller, action log, third-party-content-object-exposure log, inference module, authorization/privacy server, search module, advertisement-targeting module, user-interface module, user-profile store, connection store, third-party content store, or location store. The social-networking system 160 may also include suitable components such as network interfaces, security mechanisms, load balancers, failover servers, management-and-network-operations consoles, other suitable components, or any suitable combination thereof. In particular embodiments, the social-networking system 160 may include one or more user-profile stores for storing user profiles. A user profile may include, for example, biographic information, demographic information, behavioral information, social information, or other types of descriptive information, such as work experience, educational history, hobbies or preferences, interests, affinities, or location. Interest information may include interests related to one or more categories. Categories may be general or specific. As an example and not by way of limitation, if a user “likes” an article about a brand of shoes the category may be the brand, or the general category of “shoes” or “clothing.” A connection store may be used for storing connection information about users. The connection information may indicate users who have similar or common work experience, group memberships, hobbies, educational history, or are in any way related or share common attributes. The connection information may also include user-defined connections between different users and content (both internal and external). A web server may be used for linking the social-networking system 160 to one or more client systems 130 or one or more third-party systems 170 via a network 110. The web server may include a mail server or other messaging functionality for receiving and routing messages between the social-networking system 160 and one or more client systems 130. An API-request server may allow a third-party system 170 to access information from the social-networking system 160 by calling one or more APIs. An action logger may be used to receive communications from a web server about a user's actions on or off the social-networking system 160. In conjunction with the action log, a third-party-content-object log may be maintained of user exposures to third-party-content objects. A notification controller may provide information regarding content objects to a client system 130. Information may be pushed to a client system 130 as notifications, or information may be pulled from a client system 130 responsive to a request received from a client system 130. Authorization servers may be used to enforce one or more privacy settings of the users of the social-networking system 160. A privacy setting of a user determines how particular information associated with a user can be shared. The authorization server may allow users to opt in to or opt out of having their actions logged by the social-networking system 160 or shared with other systems (e.g., a third-party system 170), such as, for example, by setting appropriate privacy settings. Third-party-content-object stores may be used to store content objects received from third parties, such as a third-party system 170. Location stores may be used for storing location information received from client systems 130 associated with users. Advertisement-pricing modules may combine social information, the current time, location information, or other suitable information to provide relevant advertisements, in the form of notifications, to a user.

FIG. 2 illustrates an example social graph 200. In particular embodiments, the social-networking system 160 may store one or more social graphs 200 in one or more data stores. In particular embodiments, the social graph 200 may include multiple nodes—which may include multiple user nodes 202 or multiple concept nodes 204—and multiple edges 206 connecting the nodes. The example social graph 200 illustrated in FIG. 2 is shown, for didactic purposes, in a two-dimensional visual map representation. In particular embodiments, a social-networking system 160, a client system 130, or a third-party system 170 may access the social graph 200 and related social-graph information for suitable applications. The nodes and edges of the social graph 200 may be stored as data objects, for example, in a data store (such as a social-graph database). Such a data store may include one or more searchable or queryable indexes of nodes or edges of the social graph 200.

In particular embodiments, a user node 202 may correspond to a user of the social-networking system 160. As an example and not by way of limitation, a user may be an individual (human user), an entity (e.g., an enterprise, business, or third-party application), or a group (e.g., of individuals or entities) that interacts or communicates with or over the social-networking system 160. In particular embodiments, when a user registers for an account with the social-networking system 160, the social-networking system 160 may create a user node 202 corresponding to the user, and store the user node 202 in one or more data stores. Users and user nodes 202 described herein may, where appropriate, refer to registered users and user nodes 202 associated with registered users. In addition or as an alternative, users and user nodes 202 described herein may, where appropriate, refer to users that have not registered with the social-networking system 160. In particular embodiments, a user node 202 may be associated with information provided by a user or information gathered by various systems, including the social-networking system 160. As an example and not by way of limitation, a user may provide his or her name, profile picture, contact information, birth date, sex, marital status, family status, employment, education background, preferences, interests, or other demographic information. In particular embodiments, a user node 202 may be associated with one or more data objects corresponding to information associated with a user. In particular embodiments, a user node 202 may correspond to one or more webpages.

In particular embodiments, a concept node 204 may correspond to a concept. As an example and not by way of limitation, a concept may correspond to a place (such as, for example, a movie theater, restaurant, landmark, or city); a website (such as, for example, a website associated with the social-networking system 160 or a third-party website associated with a web-application server); an entity (such as, for example, a person, business, group, sports team, or celebrity); a resource (such as, for example, an audio file, video file, digital photo, text file, structured document, or application) which may be located within the social-networking system 160 or on an external server, such as a web-application server; real or intellectual property (such as, for example, a sculpture, painting, movie, game, song, idea, photograph, or written work); a game; an activity; an idea or theory; another suitable concept; or two or more such concepts. A concept node 204 may be associated with information of a concept provided by a user or information gathered by various systems, including the social-networking system 160. As an example and not by way of limitation, information of a concept may include a name or a title; one or more images (e.g., an image of the cover page of a book); a location (e.g., an address or a geographical location); a website (which may be associated with a URL); contact information (e.g., a phone number or an email address); other suitable concept information; or any suitable combination of such information. In particular embodiments, a concept node 204 may be associated with one or more data objects corresponding to information associated with concept node 204. In particular embodiments, a concept node 204 may correspond to one or more webpages.

In particular embodiments, a node in the social graph 200 may represent or be represented by a webpage (which may be referred to as a “profile page”). Profile pages may be hosted by or accessible to the social-networking system 160. Profile pages may also be hosted on third-party websites associated with a third-party server 170. As an example and not by way of limitation, a profile page corresponding to a particular external webpage may be the particular external webpage and the profile page may correspond to a particular concept node 204. Profile pages may be viewable by all or a selected subset of other users. As an example and not by way of limitation, a user node 202 may have a corresponding user-profile page in which the corresponding user may add content, make declarations, or otherwise express himself or herself. As another example and not by way of limitation, a concept node 204 may have a corresponding concept-profile page in which one or more users may add content, make declarations, or express themselves, particularly in relation to the concept corresponding to concept node 204.

In particular embodiments, a concept node 204 may represent a third-party webpage or resource hosted by a third-party system 170. The third-party webpage or resource may include, among other elements, content, a selectable or other icon, or other inter-actable object (which may be implemented, for example, in JavaScript, AJAX, or PHP codes) representing an action or activity. As an example and not by way of limitation, a third-party webpage may include a selectable icon such as “like,” “check-in,” “eat,” “recommend,” or another suitable action or activity. A user viewing the third-party webpage may perform an action by selecting one of the icons (e.g., “check-in”), causing a client system 130 to send to the social-networking system 160 a message indicating the user's action. In response to the message, the social-networking system 160 may create an edge (e.g., a check-in-type edge) between a user node 202 corresponding to the user and a concept node 204 corresponding to the third-party webpage or resource and store edge 206 in one or more data stores.

In particular embodiments, a pair of nodes in the social graph 200 may be connected to each other by one or more edges 206. An edge 206 connecting a pair of nodes may represent a relationship between the pair of nodes. In particular embodiments, an edge 206 may include or represent one or more data objects or attributes corresponding to the relationship between a pair of nodes. As an example and not by way of limitation, a first user may indicate that a second user is a “friend” of the first user. In response to this indication, the social-networking system 160 may send a “friend request” to the second user. If the second user confirms the “friend request,” the social-networking system 160 may create an edge 206 connecting the first user's user node 202 to the second user's user node 202 in the social graph 200 and store edge 206 as social-graph information in one or more of data stores 164. In the example of FIG. 2, the social graph 200 includes an edge 206 indicating a friend relation between user nodes 202 of user “A” and user “B” and an edge indicating a friend relation between user nodes 202 of user “C” and user “B.” Although this disclosure describes or illustrates particular edges 206 with particular attributes connecting particular user nodes 202, this disclosure contemplates any suitable edges 206 with any suitable attributes connecting user nodes 202. As an example and not by way of limitation, an edge 206 may represent a friendship, family relationship, business or employment relationship, fan relationship (including, e.g., liking, etc.), follower relationship, visitor relationship (including, e.g., accessing, viewing, checking-in, sharing, etc.), sub scriber relationship, superior/subordinate relationship, reciprocal relationship, non-reciprocal relationship, another suitable type of relationship, or two or more such relationships. Moreover, although this disclosure generally describes nodes as being connected, this disclosure also describes users or concepts as being connected. Herein, references to users or concepts being connected may, where appropriate, refer to the nodes corresponding to those users or concepts being connected in the social graph 200 by one or more edges 206.

In particular embodiments, an edge 206 between a user node 202 and a concept node 204 may represent a particular action or activity performed by a user associated with user node 202 toward a concept associated with a concept node 204. As an example and not by way of limitation, as illustrated in FIG. 2, a user may “like,” “attended,” “played,” “listened,” “cooked,” “worked at,” or “watched” a concept, each of which may correspond to a edge type or subtype. A concept-profile page corresponding to a concept node 204 may include, for example, a selectable “check in” icon (such as, for example, a clickable “check in” icon) or a selectable “add to favorites” icon. Similarly, after a user clicks these icons, the social-networking system 160 may create a “favorite” edge or a “check in” edge in response to a user's action corresponding to a respective action. As another example and not by way of limitation, a user (user “C”) may listen to a particular song (“Imagine”) using a particular application (SPOTIFY, which is an online music application). In this case, the social-networking system 160 may create a “listened” edge 206 and a “used” edge (as illustrated in FIG. 2) between user nodes 202 corresponding to the user and concept nodes 204 corresponding to the song and application to indicate that the user listened to the song and used the application. Moreover, the social-networking system 160 may create a “played” edge 206 (as illustrated in FIG. 2) between concept nodes 204 corresponding to the song and the application to indicate that the particular song was played by the particular application. In this case, “played” edge 206 corresponds to an action performed by an external application (SPOTIFY) on an external audio file (the song “Imagine”). Although this disclosure describes particular edges 206 with particular attributes connecting user nodes 202 and concept nodes 204, this disclosure contemplates any suitable edges 206 with any suitable attributes connecting user nodes 202 and concept nodes 204. Moreover, although this disclosure describes edges between a user node 202 and a concept node 204 representing a single relationship, this disclosure contemplates edges between a user node 202 and a concept node 204 representing one or more relationships. As an example and not by way of limitation, an edge 206 may represent both that a user likes and has used at a particular concept. Alternatively, another edge 206 may represent each type of relationship (or multiples of a single relationship) between a user node 202 and a concept node 204 (as illustrated in FIG. 2 between user node 202 for user “E” and concept node 204 for “SPOTIFY”).

In particular embodiments, the social-networking system 160 may create an edge 206 between a user node 202 and a concept node 204 in the social graph 200. As an example and not by way of limitation, a user viewing a concept-profile page (such as, for example, by using a web browser or a special-purpose application hosted by the user's client system 130) may indicate that he or she likes the concept represented by the concept node 204 by clicking or selecting a “Like” icon, which may cause the user's client system 130 to send to the social-networking system 160 a message indicating the user's liking of the concept associated with the concept-profile page. In response to the message, the social-networking system 160 may create an edge 206 between user node 202 associated with the user and concept node 204, as illustrated by “like” edge 206 between the user and concept node 204. In particular embodiments, the social-networking system 160 may store an edge 206 in one or more data stores. In particular embodiments, an edge 206 may be automatically formed by the social-networking system 160 in response to a particular user action. As an example and not by way of limitation, if a first user uploads a picture, watches a movie, or listens to a song, an edge 206 may be formed between user node 202 corresponding to the first user and concept nodes 204 corresponding to those concepts. Although this disclosure describes forming particular edges 206 in particular manners, this disclosure contemplates forming any suitable edges 206 in any suitable manner.

The popularity of e-commerce websites has fueled a rapid expansion of the number of products and services offered for sale online. Low overhead costs and a wide array of available shipping options allow e-retailers to offer the same products that are sold in brick and mortar stores, often for lower prices and with a larger selection of alternative products. Traditional brick and mortar franchises often list their products for sale online too, bringing their own market advantages to bear, such as by offering same day in-store pickup or delivery or lower prices provided by supply chain advantages of high-volume retailers. Today, nearly every product sold in the developed world is available online through numerous sellers.

Usually, a product is made available for sale online via an “item page” in a website domain. The item page contains information regarding, for example a product being offered for sale, such as, for example, the seller of the product, the location of retail chains of the seller that may carry the product, and a mechanism for a user to purchase the product over the internet (e.g., a “buy it now” or “add to cart” button). In some cases, the same product may be listed on numerous (e.g., hundreds or thousands) of e-commerce websites via different item pages that contain similar listing attributes that are often not standardized. Some websites sell products with different available options, such as different sizes or colors, and may include purchase incentives, such as free shipping or other special features. However, this information is often difficult to aggregate between listings provided by various retailers. The item pages described herein may be distinct from item listing pages that display references to and brief summaries of numerous items with links to corresponding item pages for those items.

Product listings are often presented on certain types of content pages that share one or more identifiable or common attributes. For example, a typical e-commerce website may contain the following types of pages: product detail pages, non-product pages, multiple product listing pages (e.g., listing products by category or listing products responsive to a user search), and other generic web pages (e.g., home, about, blog articles, and the like). It may be useful to distinguish between each type of page in order to analyze the data contained therein. For example, when extracting information regarding product listings, product detail pages are useful because they present a large amount of relevant information regarding each product. As another example, product listing pages are useful for iterating through all products available on a particular webpage.

However, pages of an e-commerce website are often difficult to classify or identify since considerable processing of content pages is required to extract enough useful information to make a determination as to the type of page presented. For example, product detail pages are often unstructured and non-uniform between websites. Different websites may nest product listings within varying layers of nested HTML, elements, such as multiple layers of “div” elements used in positioning product content and rendering other visual content items. As another example, a structured document, such as an XML document, that includes product listing information is provided to a mobile device application (e.g., a mobile app) for display. However, various retailers provide cataloged product information in different formats (e.g., via XML but using a different schema, syntax, or the like). Various other heterogeneous configurations for providing product listing information can be imagined. Accordingly, algorithms designed to extract product information from such listings should be adaptable to extract relevant information from any of a number of product listing formats. These algorithms may also be used to extract details regarding each product for clustering and normalization so that the extracted information can be used to improve user experience. For example, organizations often desire to use product information from different e-commerce sellers to benefit their customers. Sometimes a company providing a free service to a user, such as a social-networking service, may attempt to advertise particular products that correspond to identified interests of their users in order to continue providing that service free of charge. In some cases, this information can even be used to improve the provided service or to provide additional services to meet or surpass user expectations. The organization may receive resources from the e-commerce site in exchange for displaying an advertisement or contributing to a sale of the advertised item. This symbiotic relationship may allow the organization to pay for other resources, such as servers and employees, without charging its users. However, once product information has been extracted, an accurate and reliable mechanism for organizing that product information is required in order to productively use it.

In certain embodiments, a product listing aggregation service “crawls” content pages associated with e-commerce retailers to extract product information. “Crawling” may refer to iteration over and discovery of content pages. For example, a web crawler may crawl websites associated with a specific keyword. As another example, a crawler crawls a list of web domains. The crawler loads content associated with each identified page. In certain embodiments, content pages are classified based on identified attributes of features within the content page. The classifications can be used to formulate an algorithm tailored to crawling the particular site. For example, the algorithm may provide a smarter or more efficient means to iterate through each product listing. This is in contrast to other methods of iteration such as, for example, brute force crawling. Proper identification of product listing pages and product detail pages, as well as other types of pages, is critical to tailoring a crawling algorithm to a particular purpose, such as optimized product information extraction and cataloging.

In certain embodiments, e-commerce retailers are identified and content pages are crawled or processed to extract product listing information. For example, Big Co., a fictional department store chain having numerous franchises (e.g., brick and mortar stores) throughout the country, lists products online through its website www.bigcompany.com in addition to selling products in its retail stores. Big Co. lists products from various vendors in different departments or sections within its stores and on its website. The website contains a considerable amount of information regarding each listed products. In certain embodiments, a product catalog, such as a catalog available by subscription (e.g., the CNET product catalog), is analyzed for product listings. Product catalogs and other formats for conveying product information can be analyzed as content pages in accordance with the teachings of the present disclosure.

Often, the following product information is associated with each product listing: title, product reference number, manufacturer, color, size, model, product image(s), alternative product views, availability (e.g., units available in Big Co.'s warehouse and/or at its franchise locations), price, description, special offers, product features, noun, manufacturer's product number (MPN), universal product code (UPC), dimensions, and the like. A robust view of available product information is illustrated with reference to product panel 410 in product page 400 of FIG. 4. Big Co. may also release a product catalog in a human readable, machine readable, or any other format. For example, Big. Co. distributes a structured document that lists each of its products, available options, and list price. The catalog contains some and/or all of the product listing information available on Big. Co.'s website for each listed product.

In certain embodiments, an e-commerce company's website structure is analyzed to optimize product listing information retrieval operations. Website structure includes the layout of the site, organization of pages, architecture of the site, and the like. For example, Big Co.'s website layout can be analyzed by a classification process and a site map can be generated. A site map provides a hierarchical mapping of content pages to other related pages, and is useful for navigating through or iterating through pages of related content, such as product listing pages. In certain embodiments, a crawling process constructs a site map for a given set of web pages based on determined page types. While analyzing the structure of a given website, features of each content page are analyzed to identify elements, and the page is categorized as a particular “type” of page (e.g., product listing page, product detail page, home page, about page, and the like). For example, if a number of product listings are identified on a page, the page is classified as a product listing page.

In certain embodiments, a crawling process converts particular pages identified during a crawl into a hierarchical tree-based data structure that contains information regarding content elements associated with the page. The content elements are analyzed to identify product listings, reviews, and other identifiable features of each page. A context of identified features is determined and used to categorize pages. For example, www.bigcompany.com is structured so that product listings are accessible in multiple views on a particular page. The context of the page may relate to how products are listed within the page. The context of the page may include other attributes including size and positioning of the products, other identified features on the page, incoming and outgoing links, relational aspects of user interface items on the page, and various other page-related characteristics. A home page of bigcompany.com lists several featured items in a featured products content panel. The configuration of the content panels on the home page contributes to the context of the page and/or the context of identified features on the page. The context can be used to determine any interrelation between page features and features on the current page and/or features on additional (e.g., linked) pages. For example, clicking a featured item displayed in a home page content panel loads a new related page that lists products that are related to the home screen content. Additional pages of the bigcompany.com website showcases product listings using other content panels that are optimized for displaying large numbers of related products for comparison purposes. While the features or controls of the home page may be designed to show a random sampling of representative goods (with a goal of drawing in new users by showing available products that new users may be interested in), the page features or controls of product listing pages may be more conducive to product iteration and information extraction.

In certain embodiments, each page is analyzed to extract features that, when considered together and/or in isolation, may suggest whether the page is a product page. Product listings on the home page may be optimized to attract new users or facilitate existing user access to products, such as by enlarging a product picture and/or adding graphics, this format of listing products is not conducive to complete information extraction. In contrast, a product details page lists a comprehensive view of the product (i.e., a complete listing of product information). The comprehensive view of the product may be preferable for information extraction operations.

These differences in pages (e.g., differences between a home page and a product page) can be extracted from a DOM as features, attributes, and context information. For example, the functionality of product listing pages is designed for iteration over a set of products. Product listings and navigation buttons on each page are extracted as features. Attributes such as size and positioning of the features are extracted. Context information such as relationship and functionality of features is also extracted. Features associated with this iteration are identified as features and information regarding the context of the features is extracted to determine a classification for the page. Thus, while both “home” pages and “product listing” pages may contain product listings, categorization of each page type is possible using feature extraction and context analysis. Such an analysis allows information extraction process to optimally extract product information by targeting specific types of pages for iteration.

Page classification is useful for optimizing information extraction. For example, while blogs associated with an affiliated product line are useful for attracting new buyers, product information extraction algorithms may be better suited for examining other types of pages for product listing content, such as product listing pages and/or product details pages that are designed for product iteration and information display. The product page classifier may categorize a blog or article page as a non-product listing page (using any useful naming convention). Other pages that are better suited for information extraction operations can be inspected and classified as product pages. In certain embodiments, clues from product page features and page context can be used to identify additional product pages and may be used to classify content pages. In certain embodiments, webpage uniform resource locators (URL's) or identifiers (URI's) can be modified directly to retrieve additional relevant product or non-product pages.

With reference to FIG. 4, an example product details content page is illustrated in accordance with a non-limiting embodiment of the present disclosure. Content panel 410 includes a product feature 412 and a superimposed review feature 414. A product page classification process extracts page elements into nodes of a DOM. The DOM is analyzed to identify candidate features. For example, product feature 412 and review feature 414 are identified as candidate features. A feature is a recognizable collection of DOM nodes or user interface elements and their attributes, characteristics, and functionality, that collectively represent some portion of a content page. The recognition of these candidate features on page 400 along with context information collected from the features and other page elements provide strong evidence that page 400 is a product page. Upon further inspection, the positioning and size of product feature 412 further indicates that page 400 is a product details page. For example, product feature 412 is prominently positioned in the center of page 400 with product review information (e.g., review feature 414) below it. Such positioning, size, and relationship information is sometimes referred to as context information. However, context information may refer to information collected from other page elements, URL's, links, controls, and the like. Thus, using context information collected with reference to features 412 and 414, page 400 can be classified as a product details page.

In certain embodiments, one feature for product page classification is whether the page is linked to from another listing page. For example, since a listing page lists a number of items or products with references and links to corresponding item or product pages with more detail, the content of the item listing pages can also be analyzed to determined corresponding links to item pages. If numerous item listing pages contain links to a particular item page corresponding to the listed item, then that provides a strong indication the inbound link is the item page. Such features can be combined with numerous other features, or attributes of identified pages, and provided to a machine learning algorithm as training data for identification of other item pages, item listing pages, and the like.

As another example, the crawling algorithm retrieves another content page associated with Big Corp. This page contains information regarding various product listings. For example, with reference to FIG. 5, an example web page 500 associated with Big Corp. is illustrated in accordance with a non-limiting embodiment of the present disclosure. Web page 500 displays a content panel 510 containing a row of product listing features 512A-D and navigation panel 520. Each product feature 512A-D is represented by an image, a model name, a manufacturer, brief description, price, and color options. In this example, each product listing provides a high level overview of a men's jacket product. In certain embodiments, a web crawling process receives, for example, html instructions for building this webpage. A DOM is constructed to analyze page elements. In certain embodiments, the crawling process is provided with a more generalized URL that is manipulated to retrieve the listing of men's jackets shown in FIG. 5.

With reference to content panel 510 shown in FIG. 5, four product features 512A-D are listed in a first row. In certain embodiments, page 500 includes additional rows of product listings. The additional listings may be related to the other product listings. Page 500 also includes navigation button links 514 (i.e., “Back” and/or “Next” buttons or links), and a combo box 516 for selecting the number of items listed on each page. Collectively, this group of controls or user interface elements can be referred to as navigation feature 518. Presently, only 4 products are listed on each page; however, combo box 516 may have interactive functionality that allows an end user to select the desired number of product listings to display per page. By way of explanation, client devices connected via low bandwidth connections may load pages with less content faster than pages with large amounts of content. On certain pages, a left side column contains product attributes used for filtering product listings. With reference to webpage 500, a breadcrumb trail in navigation panel 520 shows product attribute tags leading to the current listing of products. Certain web pages may have a product attribute filtering section that allows users to select particular product attributes for narrowing and/or expanding the list of displayed products.

Context information is determined with reference to collected attributes. Context can describe relationships or functionality of features. For example, context information is determined regarding the above described operation of navigation feature 518. A page classification process can follow links associated with Back and Next buttons 514 to determine that these links iterate through a larger listing of related products. Context can include navigation end points for these links, as well as any determinations made regarding control functionality. Similarly, context information regarding placement and functionality of combo box 516 is collected. The context information in this case indicates that page 500 is a product listing page, at least because of the presence of navigation feature 518. In certain embodiments, mere positioning and size attributes of Back and Next elements or features contributes to the overall context of the features.

In certain embodiments, attributes of each feature are collected to determine a context of the feature. For example, product features 512A-D are identified by a product listing recognizer process during a page classification process. Attributes of product features 512A-D are identified. For example, size and positioning attributes of each listing are identified. The page classification process determines that these features bear a certain relationship to one another. For example, these features may be the same or similar size and may be displayed in a concerted arrangement (i.e., in a row). Alone, each of these pieces of information may be considered attributes of each feature. However, when taken together, and with reference to page 500 as a whole, this information can be used to determine a context of the features. Context information can be used to categorize a page as a product page. In the example of FIG. 5, the context information indicates that the page is a product listing page.

In certain embodiments, a product listing recognizer process identifies each of these elements to assess whether the current page is a product listing page or some other type of page. In this example, the process generates a DOM representation of the webpage and identifies candidate nodes that are potentially associated with product listings. For example, each image in 512A-D in the above described page is flagged as a candidate node. The DOM structure is traversed (e.g., up and/or down the hierarchy from each flagged candidate node) and the context of each node is analyzed. The candidate flag is confirmed or denied based on additional information gathered during the DOM traversal process that tends to include or exclude the candidate node as a product listing. The page is flagged as a product listing page based on whether the identified attributes are similar to a set of defined characteristics that define a product listing page. In certain embodiments, the characteristics include identifying more than one product listing, navigation button, listing the criteria, and the like.

With reference to FIG. 5, markup language text (e.g., HTML) representing webpage 500 is processed to generate an object model. Each element, such as product panels 512A-D, Next and Back buttons 514, combo box 516, product images, item descriptions, color selections, and each other element represented on page 500 is processed to create a hierarchical tree-based data structure. Each element is represented in the data structure as an object and each attribute of the element is represented as an attribute of that object. For example, content panel 510 contains sub-elements of product listings 512A-D. Each product listing 512A-D is hosted in its own panel (e.g., denoted by a “<DIV>” tag) that is represented in the data structure, and each sub-element of product listing 512A-D (e.g., image, description, title, model, color, etc.) is also represented in the data structure. Attributes of each element are represented as object or node attributes in the data structure. For example, the size and position of the content panel for each product listing 512A-D is specified as attributes in the data structure. In certain embodiments, the data structure is relational. For example, the data structure includes a hierarchy of object elements. Each element may have sub-elements. A root page element contains all other content within page 500.

In certain embodiments, additional page elements are identified as features and used in the product page determination based on pre-determined rules for product page identification. For example, buy buttons or links or zoom buttons (e.g., for a product image) are identified as features. A page containing one set of these features is likely a product details page while a page containing multiple sets of these features is likely a product listing page. In contrast, a page without any of these features may be considered a non-product page (e.g., a home page, about page, blog, or other non-product content page (e.g., article)). As another example, the number of product option selection features identified provides guidance as to the likelihood of a page being associated with a product. For example, if a page contains a waist size feature, an inseam feature, a color feature, and a shipment option feature, the page may likely be considered a product details page. As yet another example, the ratio of html title to link text can be used to determine whether a page is a product details page.

In certain embodiments, a “buy button” or “buy link” refers to a user interface element or collection of elements in a DOM that provide a user with a mechanism to purchase a listed product. For example, a buy link may consist of a button control that redirects the page to a payment processing page where the product can be purchased. As another example, the buy link includes client side processing that generates a message to add a particular product to a shopping cart. The shopping cart element (e.g., a widget) can be updated to include the added item. Those of ordinary skill in the art will appreciate the wide variety of content page, web page, or structured document nodes that may be associated with a buy link in the context of e-commerce product pages and listings.

Rules for product listing pages can also be specified. For example, a Next Page feature with a page number feature that indicates multiple pages (e.g., ⅕) indicates that the page is likely a product listing page. Sortable or filterable listing option features additionally indicate that the page is likely a product listing page. The presence and number of price features also is a strong indicator as to whether the page is a product page. In certain embodiments, other features such as non-product titles and anchor text provide a strong indication that the page is not associated with products.

In certain embodiments, determined rules are stored as features in a feature vector associated with a particular domain, website, page type, or the like. Other attributes of identified page types are also stored in a feature vector for each page type. In certain embodiments, a machine learning algorithm processes feature vector data to recognize similarities between identified rules and new content pages. In certain embodiments, the rule data is treated as training data and is used to train a machine learning algorithm to automatically identify pages that conform to each page type. For example, the algorithm may be able to iterate through content pages and determine what type of page each is associated with based on the determined rules.

With reference to FIG. 3, a process for extracting information from a content page to determine feature context and classify pages is illustrated in accordance with a non-limiting embodiment of the present disclosure. At step 310, a content page is identified. A data structure representing content elements in the page as nodes in a hierarchy is extracted from the content page. In certain embodiments, only features are extracted from the content page and inserted into the data structure. In certain embodiments, some combination of node/element extraction and feature extraction is performed to provide a simplified DOM structure.

As described in the example above, a web crawler crawls a set of identified web pages with the purpose of classifying pages as product or non-product pages. In certain embodiments, classification of pages is performed to optimize extraction of product content from product pages. Extracted information is stored and cataloged for use in a product aggregation service, product comparison service, or some other service or application. The web crawler retrieves content pages, such as page 500 from FIG. 5, and constructs a hierarchical tree-based data structure, or DOM, for the content page. For example, each content element, node, or feature can be represented as an object in the DOM. DOM elements can be easily traversed during processing and analyzed for associated attributes and context from related elements within the model.

At step 320, candidate features in the DOM are identified. In certain embodiments, this step includes a feature consolidation process that aggregates multiple nodes listed in the DOM into distinct features. For example, a collection of nodes that appear to function as a button or link are collected together as a feature. Similarly, a content panel hosting an organized view of data objects, such as product detail text, title, and image, can be grouped together as a candidate product feature. In certain embodiments, a product listing recognizer service is used to identify product listings. As yet another example, a navigation section for a product listing page is identified as a candidate feature. These features are referred to as candidate features because they potentially indicate whether or not the page is a product page. Certain features indicate a stronger likelihood that the current page is a product page, while certain common features provide only a slight indication that the page is a product page. In certain embodiments, the number of a particular type of feature on the page can provide another suggestion as to the type of page.

At step 330, page classification process 300 iterates through candidate features identified in the DOM. If an additional candidate feature remains, then process 300 continues on to step 340. If no candidate features remain, then process 300 proceeds to step 350. Candidate feature attributes and context are determined for each candidate feature at step 340. For example, attribute information, such as the information described in detail above, is collected for each identified candidate feature. Attributes can be collected from node or element attributes, such as html element attributes. Attributes are also often determined based on context. For example, context can include intangible attributes such as the way a feature is positioned on a page or the interaction between elements on a page. Context can also describe size ratios between features, relative positioning, prominence of a particular feature on the display (e.g., large items in the center of the display screen tend to command viewer attention), and the like. In certain embodiments, a combination of element attributes, and context is used to determine feature attributes.

At step 350, once feature attributes have been collected for identified page features, process 300 determines whether the content page is a product page. In certain embodiments, the determination is made based on a comparison of feature attributes and context to a predetermined set of characteristics that are often associated with a product page. For example, since product listing pages generally have multiple related pages with a navigation section for navigating between them and each page often lists multiple products in several rows, each of these characteristics can be defined as a rule that defines a typical product listing page or product page. In certain embodiments, as additional product pages are identified, characteristics from those page can be imported into new or existing rules. For example, as trends in product pages are detected, new characteristics are determined. New features in product pages are also identified and added to feature parsing rules for identifying candidate features in step 320.

In certain embodiments, a determination as to whether the current page is a candidate page is based on holistic consideration of all identified features. Probabilities regarding the likelihood that a particular page is a product page are weighed and assessed. For example, some features may evidence conflicting characteristics. One carousel showcase feature (e.g., as are typically found on home pages to show featured products) is identified on the same page as a navigation section to iterate through pages (e.g., as is typically found on product listing pages). In certain embodiments, identification of each feature and its context can provide an indication as to the likelihood of whether the page is a product page. For example, page context reveals that the carousel feature is located in a side panel and may be associated with an advertisement. Thus, classification process 300 may determine that the page is a product page based on context information regarding the carousel feature that mitigates its effect on the product page calculation.

DOM element context can include one or more of associated elements, attributes, functionality, appearance, and the like. For example, the size of product listings 512A-D can be analyzed to determine that 4 content panels of relative size are displayed in a row in the center of content panel 510 on page 500. This “context” can provide useful clues for identifying product listings within a page. As another example, product images within product listings 512A-D are analyzed based on size, positioning, and/or image appearance. Image recognition software can be used to identify a similar product (e.g., a jacket) in each product listing 512A-D image. As another example, product listings 512A-D can be identified using context from element relationships. For example, parent content panel hosts 4 relatively uniform content panels. Each content panel containing an image within parent content panel 512A-D is flagged as a potential “candidate” node, object, or element. In certain embodiments, name attributes are examined to determine candidate nodes. For example, if DOM object content panel 510 is titled “product panel,” the process can identify candidate nodes within the panel as potential product listings based on the name attribute context. Those of ordinary skill in the art will appreciate the wide variety of DOM objects and attributes that may serve as “context” for indicating the presence of candidate nodes that are potentially associated with product listings.

As yet another example, Next and Back buttons 514 are identified while iterating through the DOM. The mere presence of these buttons may indicate the content page is a product listing page. Moreover, links associated with these elements may be useful in crawling for additional product listings. As an additional example, the presence of combo box 516 indicates that page 500 is a product listing page. Combo box 516 may also be referenced when optimizing the iteration of products in order to efficiently retrieve a large number of products to minimize page crawling. For example, loading additional pages may be time consuming. A web crawler may prefer to load all products in a single content page for rapid iteration and cataloging. Navigation panel 520 may present additional context information for determining candidate nodes. Any other element of a web page may present context information that is useful in locating candidate features.

In certain embodiments, a product listing is recognized on an e-commerce website in connection with step 320 to identify candidate features. The product listing is processed to identify additional product listings (features) and each identified product listing page is “crawled” to find other product listings. The product listings for the website can then be modeled. The process is repeated on additional Ecommerce websites to create a data structure or model that can be searched and can help identify related products. In certain embodiments, the model can be used to help catalog additional product listings. Products can be classified and compared using, for example, title or image recognition.

In certain embodiments, product listings are recognized on a webpage and extracted using an algorithm. The algorithm involves extracting the HTML DOM of an Ecommerce webpage, and traversing it to find patterns indicative of relevant content such as product listings. CSS stylesheets can be downloaded and applied to the HTML elements and Javascript can be disabled in order to simplify crawling and product identification. Image links are downloaded and analyzed for relevancy using, for example, image recognition techniques and comparison processes.

In certain embodiments, product pages are classified based on identified features and other information they contain. For example, pages can be classified as (1) product pages, (2) non-product pages, (3) multiple product listing pages including (a) category product listing pages and (b) search result product listing pages, and (4) other, such as home or about pages. The classifier crawls web pages to identify page attributes. The page attributes are aggregated and compared to determine a type of page that is being analyzed. (e.g., product page or product listing page). Particular page types can be used in the product crawl to iterate through product listings, extract links to product pages, or identify related products.

For example, a page classifier traverses HTML elements on a web page looking for particular page features. Page features include particular DOM elements, such as buy/zoom buttons and product option elements (such as sizing information). In certain embodiments, page features can include display characteristics, such as a particular ratio between html text sizes, for example, title and link text elements. These criteria may be used to classify a particular page as a product page, product listing page, storefront, etc. For example, if a page found during a crawl exhibits particular features, it may be classified as a product page.

As another example, product listing pages can be identified as including “next page” and “last page” elements, such as in a product listing that spans multiple pages. Listing pages may also include sortable options for presenting listing results, such as price, best selling, and new arrivals. This information can be used to create a crawl for extracting information regarding all the products available on the website.

In certain embodiments, product review information is also extracted from a product listing page and included in the structure to provide more information regarding each particular product. For example, dates may be selected as pivot items, since they are commonly associated with reviews. A content element that includes rating information or some other special keywords can be used to verify parent and child nodes after locating pivot items in the HTML page. For example, a product listing has 5 reviews presented below it. This review information can be retrieved and stored with the product listing in the search structure. As another example, the search structure may be classified, and reviews can be associated with a product listing classification, such as a particular jacket by a particular manufacturer.

At step 360, if the content page is determined to be a product or item page, then the candidate page is flagged as a product page. Otherwise the candidate page is flagged as a non-product page at step 370. In certain embodiments, further analysis may be performed in accordance with process 300 to determine whether the page is another type of page. For example, the page may be analyzed to determine whether it is an item listing page or any other type of page as defined by any established rules. The process ends at step 380.

In particular embodiments, an advertisement may be text (which may be HTML-linked), one or more images (which may be HTML-linked), one or more videos, audio, other suitable digital object files, a suitable combination of these, or any other suitable advertisement in any suitable digital format presented on one or more web pages, in one or more e-mails, or in connection with search results requested by a user. In addition or as an alternative, an advertisement may be one or more sponsored stories (e.g., a news-feed or ticker item on social-networking system 160). A sponsored story may be a social action by a user (such as “liking” a page, “liking” or commenting on a post on a page, RSVPing to an event associated with a page, voting on a question posted on a page, checking in to a place, using an application or playing a game, or “liking” or sharing a website) that an advertiser promotes, for example, by having the social action presented within a pre-determined area of a profile page of a user or other page, presented with additional information associated with the advertiser, bumped up or otherwise highlighted within news feeds or tickers of other users, or otherwise promoted. The advertiser may pay to have the social action promoted. The social action may be promoted within or on social-networking system 160. In addition or as an alternative, the social action may be promoted outside or off of social-networking system 160, where appropriate. In particular embodiments, a page may be an on-line presence (such as a webpage or website within or outside of social-networking system 160) of a business, organization, or brand facilitating its sharing of stories and connecting with people. A page may be customized, for example, by adding applications, posting stories, or hosting events.

A sponsored story may be generated from stories in users' news feeds and promoted to specific areas within displays of users' web browsers when viewing a web page associated with social-networking system 160. Sponsored stories are more likely to be viewed by users, at least in part because sponsored stories generally involve interactions or suggestions by the users' friends, fan pages, or other connections. In connection with sponsored stories, particular embodiments may utilize one or more systems, components, elements, functions, methods, operations, or steps disclosed in U.S. patent application Ser. No. 13/327,557, entitled “Sponsored Stories Unit Creation from Organic Activity Stream” and filed 15 Dec. 2011, U.S. Patent Application Publication No. 2012/0203831, entitled “Sponsored Stories Unit Creation from Organic Activity Stream” and filed 3 Feb. 2012 as U.S. patent application Ser. No. 13/020,745, or U.S. Patent Application Publication No. 2012/0233009, entitled “Endorsement Subscriptions for Sponsored Stories” and filed 9 Mar. 2011 as U.S. patent application Ser. No. 13/044,506, which are all incorporated herein by reference as an example and not by way of limitation. In particular embodiments, sponsored stories may utilize computer-vision algorithms to detect products in uploaded images or photos lacking an explicit connection to an advertiser as disclosed in U.S. patent application Ser. No. 13/212,356, entitled “Computer-Vision Content Detection for Sponsored Stories” and filed 18 Aug. 2011, which is incorporated herein by reference as an example and not by way of limitation.

As described above, an advertisement may be text (which may be HTML-linked), one or more images (which may be HTML-linked), one or more videos, audio, one or more ADOBE FLASH files, a suitable combination of these, or any other suitable advertisement in any suitable digital format. In particular embodiments, an advertisement may be requested for display within third-party webpages, social-networking-system webpages, or other pages. An advertisement may be displayed in a dedicated portion of a page, such as in a banner area at the top of the page, in a column at the side of the page, in a GUI of the page, in a pop-up window, over the top of content of the page, or elsewhere with respect to the page. In addition or as an alternative, an advertisement may be displayed within an application or within a game. An advertisement may be displayed within dedicated pages, requiring the user to interact with or watch the advertisement before the user may access a page, utilize an application, or play a game. The user may, for example view the advertisement through a web browser.

A user may interact with an advertisement in any suitable manner. The user may click or otherwise select the advertisement, and the advertisement may direct the user (or a browser or other application being used by the user) to a page associated with the advertisement. At the page associated with the advertisement, the user may take additional actions, such as purchasing a product or service associated with the advertisement, receiving information associated with the advertisement, or subscribing to a newsletter associated with the advertisement. An advertisement with audio or video may be played by selecting a component of the advertisement (like a “play button”). In particular embodiments, an advertisement may include one or more games, which a user or other application may play in connection with the advertisement. An advertisement may include functionality for responding to a poll or question in the advertisement.

An advertisement may include social-networking-system functionality that a user may interact with. For example, an advertisement may enable a user to “like” or otherwise endorse the advertisement by selecting an icon or link associated with endorsement. Similarly, a user may share the advertisement with another user (e.g., through social-networking system 160) or RSVP (e.g., through social-networking system 160) to an event associated with the advertisement. In addition or as an alternative, an advertisement may include social-networking-system content directed to the user. For example, an advertisement may display information about a friend of the user within social-networking system 160 who has taken an action associated with the subject matter of the advertisement.

Social-networking-system functionality or content may be associated with an advertisement in any suitable manner. For example, an advertising system (which may include hardware, software, or both for receiving bids for advertisements and selecting advertisements in response) may retrieve social-networking functionality or content from social-networking system 160 and incorporate the retrieved social-networking functionality or content into the advertisement before serving the advertisement to a user. Examples of selecting and providing social-networking-system functionality or content with an advertisement are disclosed in U.S. Patent Application Publication No. 2012/0084160, entitled “Providing Social Endorsements with Online Advertising” and filed 5 Oct. 2010 as U.S. patent application Ser. No. 12/898,662, and in U.S. Patent Application Publication No. 2012/0232998, entitled “Selecting Social Endorsement Information for an Advertisement for Display to a Viewing User” and filed 8 Mar. 2011 as U.S. patent application Ser. No. 13/043,424, which are both incorporated herein by reference as examples only and not by way of limitation. Interacting with an advertisement that is associated with social-networking-system functionality or content may cause information about the interaction to be displayed in a profile page of the user in social-networking-system 160.

Particular embodiments may facilitate the delivery of advertisements to users that are more likely to find the advertisements more relevant or useful. For example, an advertiser may realize higher conversion rates (and therefore higher return on investment (ROI) from advertising) by identifying and targeting users that are more likely to find its advertisements more relevant or useful. The advertiser may use user-profile information in social-networking system 160 to identify those users. In addition or as an alternative, social-networking system 160 may use user-profile information in social-networking system 160 to identify those users for the advertiser. As examples and not by way of limitation, particular embodiments may target users with the following: invitations or suggestions of events; suggestions regarding coupons, deals, or wish-list items; suggestions regarding friends' life events; suggestions regarding groups; advertisements; or social advertisements. Such targeting may occur, where appropriate, on or within social-networking system 160, off or outside of social-networking system 160, or on mobile computing devices of users. When on or within social-networking system 160, such targeting may be directed to users' news feeds, search results, e-mail or other in-boxes, or notifications channels or may appear in particular area of web pages of social-networking system 160, such as a right-hand side of a web page in a concierge or grouper area (which may group along a right-hand rail advertisements associated with the same concept, node, or object) or a network-ego area (which may be based on what a user is viewing on the web page and a current news feed of the user). When off or outside of social-networking system 160, such targeting may be provided through a third-party website, e.g., involving an ad exchange or a social plug-in. When on a mobile computing device of a user, such targeting may be provided through push notifications to the mobile computing device.

Targeting criteria used to identify and target users may include explicit, stated user interests on social-networking system 160 or explicit connections of a user to a node, object, entity, brand, or page on social-networking system 160. In addition or as an alternative, such targeting criteria may include implicit or inferred user interests or connections (which may include analyzing a user's history, demographic, social or other activities, friends' social or other activities, subscriptions, or any of the preceding of other users similar to the user (based, e.g., on shared interests, connections, or events)). Particular embodiments may utilize platform targeting, which may involve platform and “like” impression data; contextual signals (e.g., “Who is viewing now or has viewed recently the page for COCA-COLA?”); light-weight connections (e.g., “check-ins”); connection lookalikes; fans; extracted keywords; EMU advertising; inferential advertising; coefficients, affinities, or other social-graph information; friends-of-friends connections; pinning or boosting; deals; polls; household income, social clusters or groups; products detected in images or other media; social- or open-graph edge types; geo-prediction; views of profile or pages; status updates or other user posts (analysis of which may involve natural-language processing or keyword extraction); events information; or collaborative filtering. Identifying and targeting users may also include privacy settings (such as user opt-outs), data hashing, or data anonymization, as appropriate.

To target users with advertisements, particular embodiments may utilize one or more systems, components, elements, functions, methods, operations, or steps disclosed in the following, which are all incorporated herein by reference as examples and not by way of limitation: U.S. Patent Application Publication No. 2009/0119167, entitled “Social Advertisements and Other Informational Messages on a Social Networking Website and Advertising Model for Same” and filed 18 Aug. 2008 as U.S. patent application Ser. No. 12/193,702; U.S. Patent Application Publication No. 2009/0070219, entitled “Targeting Advertisements in a Social Network” and filed 20 Aug. 2008 as U.S. patent application Ser. No. 12/195,321; U.S. Patent Application Publication No. 2012/0158501, entitled “Targeting Social Advertising to Friends of Users Who Have Interacted With an Object Associated with the Advertising” and filed 15 Dec. 2010 as U.S. patent application Ser. No. 12/968,786; or U.S. Patent Application Publication No. 2012/0166532, entitled “Contextually Relevant Affinity Prediction in a Social-Networking System” and filed 23 Dec. 2010 as U.S. patent application Ser. No. 12/978,265.

An advertisement may be presented or otherwise delivered using plug-ins for web browsers or other applications, iframe elements, news feeds, tickers, notifications (which may include, for example, e-mail, Short Message Service (SMS) messages, or notifications), or other means. An advertisement may be presented or otherwise delivered to a user on a mobile or other computing device of the user. In connection with delivering advertisements, particular embodiments may utilize one or more systems, components, elements, functions, methods, operations, or steps disclosed in the following, which are all incorporated herein by reference as examples and not by way of limitation: U.S. Patent Application Publication No. 2012/0159635, entitled “Comment Plug-In for Third-Party System” and filed 15 Dec. 2010 as U.S. patent application Ser. No. 12/969,368; U.S. Patent Application Publication No. 2012/0158753, entitled “Comment Ordering System” and filed 15 Dec. 2010 as U.S. patent application Ser. No. 12/969,408; U.S. Pat. No. 7,669,123, entitled “Dynamically Providing a News Feed About a User of a Social Network” and filed 11 Aug. 2006 as U.S. patent application Ser. No. 11/503,242; U.S. Pat. No. 8,402,094, entitled “Providing a Newsfeed Based on User Affinity for Entities and Monitored Actions in a Social Network Environment” and filed 11 Aug. 2006 as U.S. patent application Ser. No. 11/503,093; U.S. Patent Application Publication No. 2012/0072428, entitled “Action Clustering for News Feeds” and filed 16 Sep. 2010 as U.S. patent application Ser. No. 12/884,010; U.S. Patent Application Publication No. 2011/0004692, entitled “Gathering Information about Connections in a Social Networking Service” and filed 1 Jul. 2009 as U.S. patent application Ser. No. 12/496,606; U.S. Patent Application Publication No. 2008/0065701, entitled “Method and System for Tracking Changes to User Content in an Online Social Network” and filed 12 Sep. 2006 as U.S. patent application Ser. No. 11/531,154; U.S. Patent Application Publication No. 2008/0065604, entitled “Feeding Updates to Landing Pages of Users of an Online Social Network from External Sources” and filed 17 Jan. 2007 as U.S. patent application Ser. No. 11/624,088; U.S. Pat. No. 8,244,848, entitled “Integrated Social-Network Environment” and filed 19 Apr. 2010 as U.S. patent application Ser. No. 12/763,171; U.S. Patent Application Publication No. 2011/0083101, entitled “Sharing of Location-Based Content Item in Social-Networking Service” and filed 6 Oct. 2009 as U.S. patent application Ser. No. 12/574,614; U.S. Pat. No. 8,150,844, entitled “Location Ranking Using Social-Graph Information” and filed 18 Aug. 2010 as U.S. patent application Ser. No. 12/858,718; U.S. patent application Ser. No. 13/051,286, entitled “Sending Notifications to Users Based on Users' Notification Tolerance Levels” and filed 18 Mar. 2011; U.S. patent application Ser. No. 13/096,184, entitled “Managing Notifications Pushed to User Devices” and filed 28 Apr. 2011; U.S. patent application Ser. No. 13/276,248, entitled “Platform-Specific Notification Delivery Channel” and filed 18 Oct. 2011; or U.S. Patent Application Publication No. 2012/0197709, entitled “Mobile Advertisement with Social Component for Geo-Social Networking System” and filed 1 Feb. 2011 as U.S. patent application Ser. No. 13/019,061. Although this disclosure describes or illustrates particular advertisements being delivered in particular ways and in connection with particular content, this disclosure contemplates any suitable advertisements delivered in any suitable ways and in connection with any suitable content.

In particular embodiments, one or more of the content objects of the online social network may be associated with a privacy setting. The privacy settings (or “access settings”) for an object may be stored in any suitable manner, such as, for example, in association with the object, in an index on an authorization server, in another suitable manner, or any combination thereof. A privacy setting of an object may specify how the object (or particular information associated with an object) can be accessed (e.g., viewed or shared) using the online social network. Where the privacy settings for an object allow a particular user to access that object, the object may be described as being “visible” with respect to that user. As an example and not by way of limitation, a user of the online social network may specify privacy settings for a user-profile page that identify a set of users that may access the work experience information on the user-profile page, thus excluding other users from accessing the information. In particular embodiments, the privacy settings may specify a “blocked list” of users that should not be allowed to access certain information associated with the object. In other words, the blocked list may specify one or more users or entities for which an object is not visible. As an example and not by way of limitation, a user may specify a set of users that may not access photos albums associated with the user, thus excluding those users from accessing the photo albums (while also possibly allowing certain users not within the set of users to access the photo albums). In particular embodiments, privacy settings may be associated with particular social-graph elements. Privacy settings of a social-graph element, such as a node or an edge, may specify how the social-graph element, information associated with the social-graph element, or content objects associated with the social-graph element can be accessed using the online social network. As an example and not by way of limitation, a particular concept node 204 corresponding to a particular photo may have a privacy setting specifying that the photo may only be accessed by users tagged in the photo and their friends. In particular embodiments, privacy settings may allow users to opt in or opt out of having their actions logged by the social-networking system 160 or shared with other systems (e.g., a third-party system 170). In particular embodiments, the privacy settings associated with an object may specify any suitable granularity of permitted access or denial of access. As an example and not by way of limitation, access or denial of access may be specified for particular users (e.g., only me, my roommates, and my boss), users within a particular degrees-of-separation (e.g., friends, or friends-of-friends), user groups (e.g., the gaming club, my family), user networks (e.g., employees of particular employers, students or alumni of particular university), all users (“public”), no users (“private”), users of third-party systems 170, particular applications (e.g., third-party applications, external websites), other suitable users or entities, or any combination thereof. Although this disclosure describes using particular privacy settings in a particular manner, this disclosure contemplates using any suitable privacy settings in any suitable manner.

In particular embodiments, one or more servers 162 may be authorization/privacy servers for enforcing privacy settings. In response to a request from a user (or other entity) for a particular object stored in a data store 164, the social-networking system 160 may send a request to the data store 164 for the object. The request may identify the user associated with the request and may only be sent to the user (or a client system 130 of the user) if the authorization server determines that the user is authorized to access the object based on the privacy settings associated with the object. If the requesting user is not authorized to access the object, the authorization server may prevent the requested object from being retrieved from the data store 164, or may prevent the requested object from be sent to the user. In the search query context, an object may only be generated as a search result if the querying user is authorized to access the object. In other words, the object must have a visibility that is visible to the querying user. If the object has a visibility that is not visible to the user, the object may be excluded from the search results. Although this disclosure describes enforcing privacy settings in a particular manner, this disclosure contemplates enforcing privacy settings in any suitable manner.

FIG. 6 illustrates an example computer system 600. In particular embodiments, one or more computer systems 600 perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systems 600 provide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systems 600 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more computer systems 600. Herein, reference to a computer system may encompass a computing device, and vice versa, where appropriate. Moreover, reference to a computer system may encompass one or more computer systems, where appropriate.

This disclosure contemplates any suitable number of computer systems 600. This disclosure contemplates computer system 600 taking any suitable physical form. As example and not by way of limitation, computer system 600 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these. Where appropriate, computer system 600 may include one or more computer systems 600; be unitary or distributed; span multiple locations; span multiple machines; span multiple data centers; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 600 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems 600 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 600 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 600 includes a processor 602, memory 604, storage 606, an input/output (I/O) interface 608, a communication interface 610, and a bus 612. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 602 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor 602 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 604, or storage 606; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 604, or storage 606. In particular embodiments, processor 602 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 602 including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor 602 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory 604 or storage 606, and the instruction caches may speed up retrieval of those instructions by processor 602. Data in the data caches may be copies of data in memory 604 or storage 606 for instructions executing at processor 602 to operate on; the results of previous instructions executed at processor 602 for access by subsequent instructions executing at processor 602 or for writing to memory 604 or storage 606; or other suitable data. The data caches may speed up read or write operations by processor 602. The TLBs may speed up virtual-address translation for processor 602. In particular embodiments, processor 602 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 602 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 602 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 602. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

In particular embodiments, memory 604 includes main memory for storing instructions for processor 602 to execute or data for processor 602 to operate on. As an example and not by way of limitation, computer system 600 may load instructions from storage 606 or another source (such as, for example, another computer system 600) to memory 604. Processor 602 may then load the instructions from memory 604 to an internal register or internal cache. To execute the instructions, processor 602 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 602 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 602 may then write one or more of those results to memory 604. In particular embodiments, processor 602 executes only instructions in one or more internal registers or internal caches or in memory 604 (as opposed to storage 606 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 604 (as opposed to storage 606 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 602 to memory 604. Bus 612 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 602 and memory 604 and facilitate accesses to memory 604 requested by processor 602. In particular embodiments, memory 604 includes random access memory (RAM). This RAM may be volatile memory, where appropriate Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 604 may include one or more memories 604, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

In particular embodiments, storage 606 includes mass storage for data or instructions. As an example and not by way of limitation, storage 606 may include a hard disk drive (HDD), a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage 606 may include removable or non-removable (or fixed) media, where appropriate. Storage 606 may be internal or external to computer system 600, where appropriate. In particular embodiments, storage 606 is non-volatile, solid-state memory. In particular embodiments, storage 606 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage 606 taking any suitable physical form. Storage 606 may include one or more storage control units facilitating communication between processor 602 and storage 606, where appropriate. Where appropriate, storage 606 may include one or more storages 606. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 608 includes hardware, software, or both, providing one or more interfaces for communication between computer system 600 and one or more I/O devices. Computer system 600 may include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system 600. As an example and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces 608 for them. Where appropriate, I/O interface 608 may include one or more device or software drivers enabling processor 602 to drive one or more of these I/O devices. I/O interface 608 may include one or more I/O interfaces 608, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 610 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 600 and one or more other computer systems 600 or one or more networks. As an example and not by way of limitation, communication interface 610 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 610 for it. As an example and not by way of limitation, computer system 600 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 600 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system 600 may include any suitable communication interface 610 for any of these networks, where appropriate. Communication interface 610 may include one or more communication interfaces 610, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

In particular embodiments, bus 612 includes hardware, software, or both coupling components of computer system 600 to each other. As an example and not by way of limitation, bus 612 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCIe) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus 612 may include one or more buses 612, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

Herein, a computer-readable non-transitory storage medium or media may include one or more semiconductor-based or other integrated circuits (ICs) (such, as for example, field-programmable gate arrays (FPGAs) or application-specific ICs (ASICs)), hard disk drives (HDDs), hybrid hard drives (HHDs), optical discs, optical disc drives (ODDs), magneto-optical discs, magneto-optical drives, floppy diskettes, floppy disk drives (FDDs), magnetic tapes, solid-state drives (SSDs), RAM-drives, SECURE DIGITAL cards or drives, any other suitable computer-readable non-transitory storage media, or any suitable combination of two or more of these, where appropriate. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments described or illustrated herein that a person having ordinary skill in the art would comprehend. The scope of this disclosure is not limited to the example embodiments described or illustrated herein. Moreover, although this disclosure describes and illustrates respective embodiments herein as including particular components, elements, feature, functions, operations, or steps, any of these embodiments may include any combination or permutation of any of the components, elements, features, functions, operations, or steps described or illustrated anywhere herein that a person having ordinary skill in the art would comprehend. Furthermore, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. Additionally, although this disclosure describes or illustrates particular embodiments as providing particular advantages, particular embodiments may provide none, some, or all of these advantages. 

What is claimed is:
 1. A method comprising: by one or more processors, extracting a document object model (DOM) for a content page, wherein the DOM comprises a hierarchical tree-based data structure; by the one or more processors, traversing the DOM to identify, in the content page, candidate features of a product page; by the one or more processors, for each of the candidate features, determining candidate feature attributes based on a respective context of the candidate feature in the DOM; and by the one or more processors, determining whether the content page qualifies as a product page based on whether the candidate features fulfill a required set of characteristics for a product page.
 2. The method of claim 1, wherein determining whether the content page qualifies as a product page comprises determining whether the content page hosts one or more product listings.
 3. The method of claim 1, wherein the required set of characteristics comprises identification of at least one button associated with purchasing a product.
 4. The method of claim 1, wherein the required set of characteristics comprises identification of at least one zoom button associated with magnifying an image of a product.
 5. The method of claim 1, wherein the required set of characteristics comprises identification of more than a threshold number of available options associated with a product.
 6. The method of claim 1, wherein the required set of characteristics comprises determining whether a title associated with a product and a link associated with the product match a predetermined size ratio.
 7. The method of claim 1, further comprising, in response to determining that the content page qualifies as a product page, determining whether the product page qualifies as a product listing page based on whether the product page fulfills a predetermined set of characteristics for a product listing page.
 8. The method of claim 1, further comprising, in response to determining that the content page qualifies as a product page, determining whether the product page qualifies as a product details page based on whether the product page fulfills a predetermined set of characteristics for a product details page.
 9. The method of claim 1, wherein determining whether the candidate features fulfill the required set of characteristics for the product page comprises determining whether any of the candidate feature attributes are associated with displaying one or more products.
 10. The method of claim 1, wherein the predetermined set of characteristics for the product page comprises identification of one or more features associated with iterating over a set of products.
 11. The method of claim 1, wherein the predetermined set of characteristics for the product page comprises identification of more than a threshold number of features associated with a price.
 12. The method of claim 1, further comprising: identifying a candidate feature corresponding to a title of the content page; determining that the title of the content page is associated with a predetermined set of non-product keywords; and determining that the content page is not a product page.
 13. The method of claim 1, further comprising: in response to determining that the content page does not qualify as a product page, determining whether the content page qualifies as another page type based on whether the candidate features fulfill a required set of characteristics for the another page type.
 14. The method of claim 1, wherein determining whether the content page qualifies as a product page is further based on an inbound link analysis of other content pages associated with a domain, wherein the content page is also associated with the domain.
 15. One or more computer-readable non-transitory storage media embodying software that is operable when executed to: extract a document object model (DOM) for a content page, wherein the DOM comprises a hierarchical tree-based data structure; traverse the DOM to identify, in the content page, candidate features of a product page; for each of the candidate features, determine candidate feature attributes based on a respective context of the candidate feature in the DOM; and determine whether the content page qualifies as a product page based on whether the candidate features fulfill a required set of characteristics for a product page.
 16. The computer-readable non-transitory storage media of claim 15, wherein determining whether the content page qualifies as a product page comprises determining whether the content page hosts one or more product listings.
 17. The computer-readable non-transitory storage media of claim 15, wherein the required set of characteristics comprises identification of at least one button associated with purchasing a product.
 18. The computer-readable non-transitory storage media of claim 15, wherein the required set of characteristics comprises identification of at least one zoom button associated with magnifying an image of a product.
 19. The computer-readable non-transitory storage media of claim 15, wherein the required set of characteristics comprises identification of more than a threshold number of available options associated with a product.
 20. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: extract a document object model (DOM) for a content page, wherein the DOM comprises a hierarchical tree-based data structure; traverse the DOM to identify, in the content page, candidate features of a product page; for each of the candidate features, determine candidate feature attributes based on a respective context of the candidate feature in the DOM; and determine whether the content page qualifies as a product page based on whether the candidate features fulfill a required set of characteristics for a product page. 